Table Of ContentLecture Notes in Computer Science 6486
CommencedPublicationin1973
FoundingandFormerSeriesEditors:
GerhardGoos,JurisHartmanis,andJanvanLeeuwen
EditorialBoard
DavidHutchison
LancasterUniversity,UK
TakeoKanade
CarnegieMellonUniversity,Pittsburgh,PA,USA
JosefKittler
UniversityofSurrey,Guildford,UK
JonM.Kleinberg
CornellUniversity,Ithaca,NY,USA
AlfredKobsa
UniversityofCalifornia,Irvine,CA,USA
FriedemannMattern
ETHZurich,Switzerland
JohnC.Mitchell
StanfordUniversity,CA,USA
MoniNaor
WeizmannInstituteofScience,Rehovot,Israel
OscarNierstrasz
UniversityofBern,Switzerland
C.PanduRangan
IndianInstituteofTechnology,Madras,India
BernhardSteffen
TUDortmundUniversity,Germany
MadhuSudan
MicrosoftResearch,Cambridge,MA,USA
DemetriTerzopoulos
UniversityofCalifornia,LosAngeles,CA,USA
DougTygar
UniversityofCalifornia,Berkeley,CA,USA
GerhardWeikum
MaxPlanckInstituteforInformatics,Saarbruecken,Germany
Michael Johnson
Dusko Pavlovic (Eds.)
Algebraic Methodology
and Software Technology
13th International Conference, AMAST 2010
Lac-Beauport, QC, Canada, June 23-25, 2010
Revised Selected Papers
1 3
VolumeEditors
MichaelJohnson
MacquarieUniversity
Sydney,Australia
E-mail:[email protected]
DuskoPavlovic
UniversityofOxford
Oxford,UK
E-mail:[email protected]
LibraryofCongressControlNumber:2010941005
CRSubjectClassification(1998):D.2,F.3,D.3,F.4.1,D.2.4,D.1
LNCSSublibrary:SL2–ProgrammingandSoftwareEngineering
ISSN 0302-9743
ISBN-10 3-642-17795-6SpringerBerlinHeidelbergNewYork
ISBN-13 978-3-642-17795-8SpringerBerlinHeidelbergNewYork
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Preface
This volume contains the papers presented at AMAST 2010: the 13th Inter-
national conference on Algebraic Methodology and Software Technology. The
major goal of the AMAST conferences is to promote research that may lead to
the setting of software technology on a firm, mathematical basis. Toward this
goal, the conference supports a broad cooperation between academia and in-
dustry. The virtues of a software technologydeveloped on a mathematical basis
include the provision of software that is:
1. Correct, and the correctness can be proved mathematically
2. Safe, so that it can be used in the implementation of critical systems
3. Portable,i.e.,independentofcomputingplatformsandlanguagegenerations
4. Evolutionary, i.e., it can be self-adaptable and evolves with the problem
domain
5. Secure, so that its network and user interactions can be predicted and
controlled
The previous editions of the AMAST Conference were held at Iowa City (1989,
1991), Twente (1993), Montreal (1995), Munich (1996), Sydney (1997), Man-
aus (1999), Iowa City (2000), Reunion Island (2002), Stirling (2004), Saaremaa
(2006)andUrbana-Champaign(2008).Eachconferenceoverthelastfifteenyears
was accompanied by a proceedings volume, published in the Springer Lecture
Notes in Computer Science series.
This 13th edition of AMAST took place during June 23–26, 2010 in Lac-
Beauport, in Qu´ebec, Canada. It was was colocated with MPC 2010: the 10th
InternationalConferenceonMathematicsofProgramConstruction,heldduring
June 21–23, 2010. There were 33 submissions. Each submission was reviewed
by at least three, and on the average 3.9, Program Committee members. The
committee decided to acceptten full-length researchpresentationsandfour sys-
temdemonstrations.Theprogramalsoincludedtwoinvitedtalks,givenbyJane
Hillston (Edinburgh University), and Catuscia Palamidesi (INRIA). Jane Hill-
ston also provided a paper for Part 1 of this volume. The contributed research
papers are in Part 2 and Part 3 contains the system demonstrations.
We aregratefulto the members ofthe ProgramCommittee andthe external
refereesfortheircareanddiligenceinreviewingthesubmittedpapers,andtothe
staff of Springer-Verlag.The review process and compilation of the proceedings
were greatly helped by Andrei Voronkov’s EasyChair system.
August 2010 Michael Johnson
Dusko Pavlovic
Conference Organization
Program Chairs
Michael Johnson Macquarie University, Australia
Dusko Pavlovic KestrelInstitute,USAandUniversityofOxford,UK
Program Committee
Paolo Baldan Dipartimento di Matematica Pura e Applicata,
Universita´ di Padova,Italy
Gilles Barthe IMDEA Software, Spain
Michel Bidoit INRIA Saclay, France
Manfred Broy TUM, Germany
Roberto Bruni University of Pisa, Italy
Iliano Cervesato Carnegie Mellon University - Qatar Campus,
Qatar
Adriana Compagnoni Stevens Institute of Technology, USA
Jos´e Luiz Fiadeiro University of Leicester, UK
Kokichi FUTATSUGI JAIST, Japan
Rob Goldblatt Victoria University of Wellington, New Zealand
Ichiro Hasuo RIMS, Kyoto University, Japan
Rolf Hennicker Ludwig-Maximilians-Universita¨tMu¨nchen,
Germany
H´el`ene Kirchner INRIA, France
Barbara Ko¨nig Universita¨t Duisburg-Essen, Germany
Narciso Marti Oliet Universidad Complutense de Madrid, Spain
Michael Mislove Tulane University, USA
Larry Moss Department of Mathematics, Indiana
University, Bloomington, USA
Till Mossakowski DFKI Lab Bremen, Germany
Peter D. Mosses Swansea University, UK
Andrzej Murawski University of Oxford, UK
Uwe Nestmann Technische Universit¨at Berlin, Germany
Fernando Orejas UPC, Spain
Leila Ribeiro Universidade Federal do Rio Grande do Sul,
Brazil
Grigore Rosu University of Illinois at Urbana-Champaign,
USA
Jan Rutten CWI, The Netherlands
VIII Conference Organization
Lutz Schr¨oder DFKI Bremen and Universita¨t Bremen,
Germany
Wolfram Schulte Microsoft Research, USA
Douglas Smith Kestrel Institute, USA
Carolyn Talcott SRI International, USA
Andrzej Tarlecki Institute of Informatics, Faculty of
Mathematics, Informatics and Mechanics,
Warsaw University, Poland
Varmo Vene University of Tartu, Estonia
E.P. de Vink Technische Universiteit Eindhoven,
The Netherlands
James Worrell University of Oxford, UK
External Reviewers
Ludwig Adam Dieter Hutter
Sebastian Bauer Stefan Kiefer
Laura Bocchi Ekaterina Komendantskaya
Jewgenij Botaschanjan Dexter Kozen
Marzia Buscemi C´esar Kunz
Yuki Chiba Alberto Lluch Lafuente
Mihai Codescu Masaki Nakamura
Andrea Corradini Kazuhiro Ogata
Silvia Crafa Catuscia Palamidessi
Vijay D’silva Kirstin Peters
Tobias Eibach Ricardo Pen˜a
Cristian Ene Erik Poll
Jean-Christophe Filliˆatre Bernhard Reus
Reiner H¨ahnle Mehrnoosh Sadrzadeh
Daniel Hedin Francesco Tapparo
Torsten Hildebrandt David Trachtenherz
Cl´ement Hurlin Virginie Wiels
Local Organizers
Claude Bolduc, Jules Desharnais, and B´echir Ktari (Universit´e Laval, Canada)
Sponsoring Institutions
– Universit´e Laval, Qu´ebec, Canada, http://www.ulaval.ca
– Centre de recherches math´ematiques, Universit´e de Montr´eal, Montr´eal,
Canada, http://www.crm.umontreal.ca
Table of Contents
Part 1. Invited Paper
Structural Analysis for Stochastic Process Algebra
Models (Invited Talk) ............................................ 1
Jie Ding and Jane Hillston
Part 2. Contributed Research Papers
Verification of Common Interprocedural Compiler Optimizations Using
Visibly Pushdown Kleene Algebra.................................. 28
Claude Bolduc and B´echir Ktari
On the Expressiveness of the π-Calculus and the Mobile Ambients ..... 44
Linda Brodo
Integrating Maude into Hets....................................... 60
Mihai Codescu, Till Mossakowski, Adria´n Riesco, and
Christian Maeder
Model Refinement Using Bisimulation Quotients ..................... 76
Roland Glu¨ck, Bernhard Mo¨ller, and Michel Sintzoff
Type Fusion..................................................... 92
Ralf Hinze
Coalgebraic Semantics for Parallel Derivation Strategies in Logic
Programming.................................................... 111
Ekaterina Komendantskaya, Guy McCusker, and John Power
Learning in a Changing World, an Algebraic Modal Logical
Approach ....................................................... 128
Prakash Panangaden and Mehrnoosh Sadrzadeh
Matching Logic: An Alternative to Hoare/FloydLogic ................ 142
Grigore Ro¸su, Chucky Ellison, and Wolfram Schulte
ProgramCalculation in Coq....................................... 163
Julien Tesson, Hideki Hashimoto, Zhenjiang Hu,
Fr´ed´eric Loulergue, and Masato Takeichi
Cooperation of Algebraic Constraint Domains in Higher-Order
Functional and Logic Programming ................................ 180
Rafael del Vado V´ırseda
X Table of Contents
Part 3. System Demonstrations
Proving Termination Properties with mu-term ...................... 201
Beatriz Alarc´on, Rau´l Guti´errez, Salvador Lucas, and
Rafael Navarro-Marset
BAL Tool in Flexible Manufacturing Systems........................ 209
Diego P´erez Lea´ndrez, M. Carmen Ruiz, J. Jose Pardo, and
Diego Cazorla
A Complete Declarative Debugger for Maude........................ 216
Adria´n Riesco, Alberto Verdejo, and Narciso Mart´ı-Oliet
An Assume Guarantee Approach for Checking Quantified Array
Assertions....................................................... 226
Mohamed Nassim Seghir
Author Index.................................................. 237
Structural Analysis for Stochastic Process
Algebra Models
Jie Ding1 and Jane Hillston2
1 School of Information Engineering, Yangzhou University,Yangzhou, 225009, China
[email protected]
2 LFCS, School of Informatics, Edinburgh University,UK
[email protected]
Abstract. Stochastic process algebra models have been successfully
usedintheareaofperformancemodellingforthelasttwentyyears,and
more recently have been adopted for modelling biochemical processes
in systems biology. Most research on these modelling formalisms has
been on quantitative analysis, particularly the derivation of quantified
dynamicinformation about thesystem modelled intheface of thestate
space explosion problem. In this paper we instead consider qualitative
analysis,lookingathowrecentdevelopmentstotacklestatespaceexplo-
sion in quantified analysis can be also harnessed to establish properties
such as freedom from deadlock in an efficient manner.
1 Introduction
Stochastic process algebras were introduced in the early 1990s as a formal
modelling formalism for performance modelling which allowed continuous time
Markovchains(CTMCs)tobespecifiedinarigorous,compositionalmanner(see
for example [1, 10, 12]). Like all discrete state modelling formalisms the process
algebra models suffered from problems of state space explosion when represent-
ing large or complex systems, and subsequent work focussed on exploiting the
compositionalityofthe processalgebrato decomposeorsimplify the underlying
CTMC e.g. [2, 5, 11, 15, 19, 22].
More recently stochastic process algebras such as the stochastic π-calculus
[16, 20] and PEPA [3, 4] have been used for modelling biochemical mechanisms
withincells.Inthesecasesthestatespaceexplosionproblembecomesalmostin-
surmountable.Consequentlyinmanycasesmodelsareanalysedbydiscreteevent
simulationratherthan being able to abstractlyconsider allpossible behaviours.
However an alternative approach has emerged which is suitable for models
which are comprised of large numbers of repeated components, fluid approxi-
mation [13]. In this approach an alternative state representation is chosen, and
system dynamics are approximated by a continuous updating of state rather
than the usual discrete steps which are represented in process algebra seman-
tics. Whereas process algebra semantics usually capture the structure of the
system in terms of interacting components and eachof their localstates, in this
M.JohnsonandD.Pavlovic(Eds.):AMAST2010,LNCS6486,pp.1–27,2011.
(cid:2)c Springer-VerlagBerlinHeidelberg2011
2 J. Dingand J. Hillston
approachonlyaaggregationiscapturedwhichcountsthenumberofcomponents
which currently exhibit a particular behaviour [25].
Whilst the focus of stochastic process algebras has understandably been pri-
marily quantitative analysis, qualitative analysis can also provide valuable in-
sight into the behaviour of a system. In contrast, in Petri net modelling there
are well-established techniques of structural analysis [6, 9, 17, 23, 24]. In this
paper we show how the new state representationschema for the stochastic pro-
cessalgebraPEPA,developedto supportfluidapproximation,makesitpossible
to readily adapt structural analysis techniques for Petri nets to PEPA. More-
over, the compact representation form means that qualitative analysis can now
be applied to systems of a size and complexity which could not previously be
considered.
The remainder of this paper is organised as follows. Section 2 introduces
PEPA and the numerical representationschema used throughout this paper. In
Section 3 we explainhow this representationmakes apparentthe P/Tstructure
underlying every PEPA model and in Section 4 we show how this structure can
be used to uncover invariants of a model. In Section 5 and 6 we discussed lin-
earisedstatespaceandanewdeadlockcheckingalgorithmrespectively.Section7
presents some related work and we conclude in Section 8.
2 The PEPA Modelling Formalism
This section will briefly introduce the PEPA language and its numerical repre-
sentationscheme.ThenumericalrepresentationschemeforPEPAwasdeveloped
by Ding in his thesis [7], and represents a model numerically rather than syn-
tactically supporting the use of mathematical tools and methods to analyse the
model.
2.1 Introduction to PEPA
PEPA (PerformanceEvaluation Process Algebra) [12], developed by Hillston in
the 1990s, is a high-level model specification language for low-level stochastic
models, and describes a system as an interaction of the components which en-
gageinactivities.Incontrasttoclassicalprocessalgebras,activitiesareassumed
tohaveadurationwhichisarandomvariablegovernedbyanexponentialdistri-
bution.ThuseachactivityinPEPAisapair(α,r)whereαistheactiontypeand
ristheactivityrate.Thelanguagehasasmallnumberofcombinators,forwhich
we provide a brief introduction below; the structured operationalsemantics can
be found in [12]. The grammar is as follows:
S ::=(α,r).S |S+S |CS
(cid:2)(cid:3)
P ::=P P |P/L|C
L
whereSdenotesasequentialcomponent andP denotesamodelcomponent which
executes in parallel. C stands for a constant which denotes either a sequential
component or a model component as introduced by a definition. CS stands for
